Research on Bayesian Damage Identification Method Based on Radial Basis Neural Network Surrogate Model
A method was proposed to use radial basis function neural networks as surrogate models for damage iden-tification within the Bayesian framework.Initially,Latin hypercube sampling was employed to select a specific number of structural input-output samples,leading to the training of a radial basis function neural network.Subsequently,this network was applied to a Bayesian damage identification method based on Markov chain Monte Carlo sampling.Gibbs sampling was utilized as the sampling method.Numerical examples demonstrated that,considering measurement er-rors,the proposed method accurately identified damage in simply supported beams,effectively avoiding the ill-posed na-ture of the inverse problem in damage identification.The computational efficiency of this method was improved by sev-eral orders of magnitude compared to traditional approaches,making it a highly promising damage identification method.
radial basis neural networkdamage identificationMarkov Chain Monte CarloGibbs sampling
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维普
